Update app.py

app.py

@@ -0,0 +1,72 @@
+import streamlit as st
+import pandas as pd
+import os
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+import groq
+
+# Initialize Groq API
+groq_client = groq.Client(api_key="your_groq_api_key")
+
+# Load RAG components
+retriever_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
+retriever_model = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
+generator_tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large")
+generator_model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large")
+
+# Function to process user input and generate financial statements
+def generate_financial_statements(file, file_type):
+    # Read the file
+    if file_type == "csv":
+        df = pd.read_csv(file)
+    elif file_type == "excel":
+        df = pd.read_excel(file)
+    else:
+        st.error("Unsupported file type. Please upload a CSV or Excel file.")
+        return
+
+    # Convert the data into a context string
+    context = df.to_string()
+
+    # Define financial statement queries
+    queries = [
+        "Generate a journal from the following financial data:",
+        "Generate a general ledger from the following financial data:",
+        "Generate an income statement from the following financial data:",
+        "Generate a balance sheet from the following financial data:",
+        "Generate a cash flow statement from the following financial data:"
+    ]
+
+    # Generate financial statements using RAG
+    financial_statements = {}
+    for query in queries:
+        # Combine query and context
+        input_text = f"{query}\n{context}"
+
+        # Retrieve relevant information (optional, if using a retriever)
+        input_ids = retriever_tokenizer(input_text, return_tensors="pt").input_ids
+        retrieved_context = retriever_model(input_ids)
+
+        # Generate response using the generator model
+        input_ids = generator_tokenizer(input_text, return_tensors="pt").input_ids
+        output = generator_model.generate(input_ids)
+        response = generator_tokenizer.decode(output[0], skip_special_tokens=True)
+
+        # Store the result
+        financial_statements[query] = response
+
+    return financial_statements
+
+# Streamlit UI
+st.title("Financial Statement Generator")
+st.write("Upload your financial data (CSV or Excel) to generate journal, general ledger, income statement, balance sheet, and cash flow statement.")
+
+# File upload
+uploaded_file = st.file_uploader("Upload your file", type=["csv", "xlsx"])
+if uploaded_file is not None:
+    file_type = uploaded_file.name.split(".")[-1]
+    financial_statements = generate_financial_statements(uploaded_file, file_type)
+
+    # Display results
+    for statement_type, statement in financial_statements.items():
+        st.subheader(statement_type)
+        st.write(statement)